Partner Portal Search & Recommendation Enhancements - Vector Intern Talks
The goal of this project was to deliver more relevant content (and search results) to users across different industries and technical expertise levels on Vector's Partner Portal website. To achieve this, I deployed a Flask API on GCP that received search queries and user details from the portal, and returned tailored content recommendations and more insight search results. The API leveraged a Qdrant vector database, maintained and updated through a data pipeline, ensuring users received the most current and relevant content. The project resulted in a significantly improved website experience, offering tailored recommendations and a search engine that went beyond simple keyword matching. This new system provided users with faster, more meaningful results across 1,500+ pieces of content—including blog posts, resources, videos, and publications—with continued growth.
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